Abstract Data Graph for Web Component Rendering Optimization
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Solution Overview
Problem
Conventional techniques for accessing and updating data in web-based user interfaces lead to lag and delay due to serializing the process of GUI component rendering and data retrieval, especially when data is dynamically determined at render time, resulting in slow loading times and inefficient data fetching.
Innovation Solution
The implementation of static dependency analysis to construct an Abstract Data Graph (ADG) describing the data requirements of GUI components, allowing for preemptive data retrieval and caching, and the use of progressive page rendering to link data resolution with user interface presentation, enabling data to be retrieved only when needed, thus reducing unnecessary data retrieval and improving performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data retrieval is performed during GUI component rendering, then the user interface can be dynamically updated with current data, but the rendering process experiences lag and delay due to serialization
Solution Approach 1:
The system performs static dependency analysis to construct an Abstract Data Graph (ADG) that identifies all data dependencies before rendering begins. This preliminary analysis enables the system to pre-fetch data in advance, eliminating the need to wait for data during the rendering process and thus resolving the contradiction between data accuracy and rendering speed.
Solution Approach 2:
The rendering process is segmented into multiple phases: (1) static dependency analysis phase where the ADG is constructed, (2) data resolution phase where data is fetched based on the ADG, and (3) rendering phase where the GUI is assembled. This segmentation allows data retrieval to occur separately from rendering, eliminating serialization delays while ensuring data accuracy.
2Speed
If all data is retrieved preemptively to speed up rendering, then GUI rendering is faster, but unnecessary data retrieval increases server requests and reduces efficiency
Solution Approach 1:
The Abstract Data Graph is constructed in advance to identify exactly which data is needed for rendering. This preliminary action enables precise data fetching - only the specific data nodes identified in the ADG are retrieved from the server, avoiding both premature data retrieval and unnecessary server requests while maintaining fast rendering.
3Adaptability or versatility
If data is retrieved dynamically at render time, then the system can adapt to changing data requirements, but loading times increase due to sequential processing
Solution Approach 1:
The static dependency analysis constructs the ADG in advance, identifying all data requirements before rendering. This preliminary action allows the system to adapt to changing data requirements while avoiding sequential processing delays, as the dependency map is ready beforehand to guide parallel or optimized data retrieval.
Solution Approach 2:
The system dynamically resolves data based on the pre-constructed ADG, allowing flexibility in data retrieval while maintaining efficiency. The ADG serves as a dynamic guide that adapts to specific rendering needs without requiring sequential processing, resolving the contradiction between adaptability and loading time.
Data Source
AI summary
An abstract data graph may be constructed at a server. The abstract data graph may include nodes and links between nodes and may represent computer programming instructions for generating a graphical user interface at a client machine. At least some of the links may represent dependency relationships between portions of the graphical user interface. The abstract data graph may be resolved at the client machine to identify data items, which may be retrieved from the server and used to render the graphical user interface.